Inside a Neural Network: Neurons, Weights, and Biases Explained

Hey folks,

Hope you are doing good! Last week we covered what neural networks are - Understanding Neural Networks: The tech behind today's AI. Today we are diving deeper into how they actually work inside.

Neural networks get their name because they work like our brain. So let me explain using an analogy of how the human brain learns.

How Our Brain Learns

Imagine a baby seeing an apple for the first time. They don't know what it is. But after seeing it multiple times and tasting it, they learn what an apple is. Their brain recognizes the pattern - the color, the shape, the taste.

This happens because brain cells called neurons get activated. Every time the baby sees an apple, these neurons activate more and more. The brain is learning the pattern of an apple.

Artificial Neural Networks work the same way. They are literally named after the brain because they copy how the brain learns.

The Structure of Neural Networks

An artificial neural network has multiple neurons connected together in layers:

• Input layer (receives data)
• Hidden layers (learns patterns)
• Output layer (gives answer)

Think of it like a relay race. Data runs through the input layer, gets passed through the hidden layers where learning happens, and finally reaches the output layer with an answer.

What is a Neuron?

A neuron is the smallest unit in a neural network. It's a function that takes inputs and produces an output between 0 and 1.

Different neurons are responsible for recognizing different patterns. For example:

• Some neurons recognize the red color in an apple
• Some neurons recognize the round shape
• Some neurons recognize the size

When a neuron sees its pattern, it activates (like turning on a light). This activation is called an activation function.

So basically: Each neuron = one activation function

The Layers Explained

Input Layer: Receives raw data (like pixels in an image). The number of neurons here equals the number of input features.

Example: If you're identifying apples from photos, and each photo has 28×28 pixels, you have 784 neurons in the input layer (28 × 28 = 784).

Hidden Layers: These are where the learning happens. They find patterns in the data.

• Can have one or many hidden layers and this count depends on how complex your problem is. For example,
  • Recognizing a number? 1-2 hidden layers
  • Recognizing a fruit? Maybe 3-4 layers
  • Recognizing a person's face? More layers

Output Layer: Gives the final answer. Has one neuron with a value between 0 and 1.

Example: If predicting "apple or not apple", output is 0.95 (95% sure it's an apple).

Connections Between Neurons: Weights

The connections between neurons have values called weights.

Think of weights like volume knobs:

• High weight = that input is important (loud signal)
• Low weight = that input is not important (quiet signal)
• Zero weight = ignore this input completely

When data flows from one neuron to the next, the weight decides how much that data matters.

Adjusting the Neuron: Bias

A bias is an extra value we add after multiplying by weights. It helps the neuron make better decisions.

Think of bias like an offset or adjustment:

• Weights multiply the input
• Bias shifts the result

Example: Predicting house prices

• Weight for house size: 1000 (size matters a lot)
• Bias: 50,000 (base price is 50k even for size 0, because land costs money)

The Formula

Every neuron does this calculation:

Output = (Input1 × Weight1) + (Input2 × Weight2) + (Input3 × Weight3) + ... + Bias
Final Output = Activation Function (Output)

Real example with numbers:

Input: [0.5, 0.3, 0.8]
Weights: [2, 3, 1]
Bias: 0.5

Calculation:
(0.5 × 2) + (0.3 × 3) + (0.8 × 1) + 0.5
= 1.0 + 0.9 + 0.8 + 0.5
= 3.2

After activation function: 0.96 (converted to 0-1 range)

How Do Weights Get Their Values?

Initially: Weights start as random numbers. The network has no idea what the right values should be yet.

During Training: The network makes a prediction, compares it to the actual answer, and sees how wrong it was. Then it adjusts the weights - a little bit at a time to make the next prediction better.

After many training iterations: The weights become "trained weights" that are actually useful. The network learned what values work best for recognizing patterns.

That's the foundation! Neural networks are just neurons in layers, passing data through weights and biases, learning patterns along the way.

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